Dimensions of Collective Intelligence and Knowledge Sharing in Moroccan Associations: A Structural Analysis

Abstract

This study examines the main dimensions of collective intelligence within Moroccan associations, a sector in full expansion, which has more than 259,000 associations in 2022. The objective of the research is to identify, prioritize, and validate the main dimensions of collective intelligence within Moroccan associations. A multi-tool research methodology was adopted, combining the review of scientific literature, qualitative interviews, and a quantitative study of 125 Moroccan associations. The results highlight six key dimensions of collective intelligence: social sensitivity, autonomy, motivation, transformational leadership, collaboration, and knowledge sharing. Among the dimensions identified, collaboration and leadership have the highest relative weights in structuring collective intelligence. Social sensitivity and knowledge sharing complement this structuring by promoting quality interactions, fluid communication, and the mobilization of distributed knowledge. Motivation and autonomy are also essential dimensions, supporting the commitment of members and the expression of diverse perspectives within associations. The study provides theoretical contributions by adapting collective intelligence models to the peculiarities of Moroccan civil society. At the applied level, the study proposes a set of recommendations, including strengthening capacity building, developing leadership skills, and adopting collaborative digital platforms. The results in their entirety confirm the strategic importance of collective intelligence in improving the effectiveness of Moroccan associations and enhancing their impact in the face of increasingly complex social challenges.

1. Introduction

Collective intelligence is increasingly considered a key mechanism enabling organizational and social innovation by facilitating knowledge integration and collective problem solving. In this context of civil society organizations, these mechanisms are particularly important for coordination, learning, and organizational effectiveness.

Since the early 2000s, Morocco has experienced a remarkable boom in terms of the number of associations created. Therefore, this number has reached 259,000 associations [1] until March 2022, covering the entire national territory and opening in various fields (sustainable development, sport, education, health, drinking water, culture, environment, etc.). Thus, the dynamic that characterizes this sector testifies to the desire of citizens and the public authorities to achieve objectives of general interest.

However, these organizations, necessary for the development of a dynamic civil society in Morocco, are facing considerable challenges that limit their progress and influence. Although associations have the right to benefit from public support, a large majority have only low budgets, mainly from the contributions of their members [2]. In addition, weak leadership, poor internal governance, and lack of professionalism are another challenge, in addition to the difficulties of networking and collaboration with other associations to overcome insufficient resources [2].

Faced with this observation, the strengthening of collective intelligence appears as a strategic lever to increase performance and improve the role of associations. In this context, the report on the new development model in Morocco highlights the importance of the participation of all actors in the country, especially civil society, to face the expanded complexity of problems, the resolution of which requires a systemic approach that mobilizes the collective intelligence of all actors [3]. However, collective intelligence can be defined as “the ability of a group for solving problems collaboratively by mixing the knowledge and expertise of group members” [4]. It also indicates “the ability of a group of people, through concerted reflection among its members, to lead to a better solution to a problem that cannot be achieved if these members work alone” [5].

This study seeks to understand how collective intelligence is structured and manifested within Moroccan associations: What are the main dimensions that characterize collective intelligence in the Moroccan associative environment?

Thus, this study aims to identify, prioritize, and validate the main constituent dimensions of collective intelligence within Moroccan associations in order to propose courses of action that can strengthen the organizational capacities of these actors of local development. The originality of this research lies in the identification, prioritization, and validation of the main dimensions of collective intelligence within Moroccan associations. It contributes to the enrichment of knowledge by proposing an analytical framework adapted to the context of Moroccan civil society and by shedding light on how collective intelligence is structured and manifested in this specific context. It thus contributes to enriching both the understanding of this phenomenon in the Moroccan context and the academic literature devoted to collective intelligence.

This research aims to develop and validate a scale of measurement of the constituent dimensions of collective intelligence in Moroccan associations. To this end, an in-depth review of the literature enabled the measurement items to be generated before a quantitative study based on a questionnaire administered to the associative leaders was carried out. The data collected were then mobilized to assess the psychometric qualities of the scale and validate a reliable and valid measuring instrument, adapted to the specific context of Moroccan associations.

In order to carry out this study, the following section proposes a theoretical framework on the potential dimensions of collective intelligence. Section 3 is devoted to the presentation of the research methodology adopted during our research. Next, the research results are presented in Section 4, while their interpretation is explored in depth in Section 5. Finally, in Section 6, the conclusion focuses on the main theoretical and practical implications, while highlighting the limits and perspectives of research.

2. Theoretical Framework: Highlighting the Dimensions of Collective Intelligence

Collective intelligence is a concept that has attracted increasing interest since the 1990s, particularly with the development of information and communication technologies. Many researchers have proposed different theoretical approaches to define and analyze it. This literature review examines the main conceptualizations of collective intelligence through the work of [6,7,8,9,10]. These contributions highlight the diversity of perspectives mobilized to understand this concept. In this research, collective intelligence is approached from an organizational and multidimensional perspective, the different dimensions selected being derived from the literature and integrated into a latent second-order construct adapted to the context of Moroccan associations.

Before delving into the main theoretical perspectives on collective intelligence, it is necessary to present the main dimensions retained in this research. From the literature, these dimensions correspond to the constituent components of collective intelligence from an organizational perspective. They include social sensitivity, autonomy, motivation, leadership, collaboration, and knowledge sharing. These dimensions will serve as a common thread for the analysis of the different theoretical approaches and for the construction of the proposed conceptual model.

2.1. Social Sensitivity

Social sensitivity, defined as the ability to accurately perceive and decode the social and emotional signals of others [11], has been identified by several studies as a key factor influencing collective intelligence within teams [6]. Therefore, a high level of social sensitivity within a team seems to promote better communication, cooperation, and coordination among members, which translates into greater collective effectiveness in solving problems [12]. Social sensitivity facilitates harmonious social interactions and team cohesion [13].

Several studies have shown that the average social sensitivity of a team predicts its overall performance in various cognitive tests reflecting its collective intelligence [12]. However, some studies have not found a significant link between social sensitivity and team performance in specific contexts such as software projects or virtual teams [14]. Several studies also associate an increased social sensitivity of teams composed of a greater proportion of women [15].

Women would tend to better decode social interactions, which would strength collective intelligence. Therefore, despite certain contextual limitations, social sensitivity appears as a key dimension of the effectiveness of interactions within teams and their overall collective intelligence. To this end, our first hypothesis is reformulated in Table 1.

Table 1. Research hypotheses.

Code	Variables	Formulation
H1	Social sensitivity	Social sensitivity is a significant dimension of collective intelligence within Moroccan associations.
H2	Professional autonomy	The autonomy of members is a dimension of collective intelligence within Moroccan associations.
H3	Motivation	Intrinsic motivation is a constituent dimension of collective intelligence within Moroccan associations.
H4	Leadership	Transformational leadership is a constituent dimension of collective intelligence within Moroccan associations.
H5	Involvement at work (collaboration)	Collaboration between members is a constituent dimension of collective intelligence within Moroccan associations.
H6	Knowledge sharing	Knowledge sharing among members is a constituent dimension of collective intelligence within Moroccan associations.

2.2. Autonomy

Professional autonomy is one of the characteristics of work that has been widely studied in literature. It is defined as the degree of freedom and independence available to an employee to plan work and determine procedures [16]. It also refers to an employee’s ability to determine the place, sequence, and methods for performing assigned tasks [17]. Therefore, self-determination theory states that the satisfaction of the fundamental psychological needs of employees, including autonomy, leads to the maintenance of their well-being while the opposite causes motivational exhaustion [18]. It is defined as one of the key components of the organizational environment, promoting creativity, innovation [19], and agility [20].

Therefore, many studies reported the positive impact of autonomy on the creative thinking and on teams’ organizational performance [21]. Langfred [22] concluded that the team autonomy has a positive impact on performance when interdependence is high, unlike individual autonomy, which has negative impact on performance when interdependence is high [22]. For his part, Zaibet [23] considers autonomy in his model of collective action that must be present both at the individual level and at the team level [23].

For his part, James Surowiecki [7] emphasizes in his model of crowd wisdom the importance of the independence of individuals in collective intelligence, specifying that one of the reasons for the success of collective decision is the fact that people pay less attention of others say. It states that independence is the most important standard for intelligent collectives.

In his study about choosing the new nesting websites by bee swarms, [24] specifies that many independent evaluations acquired privately provide the swarm with a precise assessment of the quality of each potential nesting site and avoids the recurrent amplification of publicly acquired information that can lead to information cascades (sheep’s behaving). To this end, our second hypothesis is reformulated in Table 1.

2.3. The Motivation

The motivation is a key element of collective intelligence [25]. Literature distinguishes between intrinsic motivation (pleasure in the activity and meaning at work) and extrinsic motivation [26]. Therefore, several studies have highlighted the role of intrinsic motivation in the development of employee well-being, creativity, and organizational performance [26,27]. As well, others have highlighted the superiority of intrinsic motivation over extrinsic motivation to manage creative work by specifying that creativity is based on intrinsic motivation rather than financial rewards or bonuses [28,29].

As a result, self-determination theory emphasizes the multidimensional nature of motivation in the workplace (from extrinsic to intrinsic) and its influence on the behavior and well-being of employees [27]. Similarly, a study conducted with volunteer contributors to the trove project of the National Library of Australia revealed the dynamic nature of motivation over time, moving from mostly intrinsic factors (initial participation) to a combination of intrinsic and extrinsic (continuous participation) [30]. In the same vein, Tjornbo [10] states that motivation is a key factor for collective intelligence to support social innovation.

Finally, motivation proves to be a determining element in the emergence and effectiveness of collective intelligence within organizations [31]. This is built through the combination of the different organizational “genes,” for instance, motivation (love and glory), playing a crucial role in the engagement and contribution of individuals within innovative collective systems, for example, Google, Wikipedia, and Threadless [32]. To this end, our third hypothesis is reformulated in Table 1.

2.4. Leadership

Effective leadership is highlighted in the literature as an important factor in collective intelligence given its role in developing cognitive diversity, promoting independence, and facilitating access to decentralized knowledge and effective aggregation of knowledge, which allows a multitude of diverse individuals to surpass even the best experts in problem solving and innovation [33]. In addition, effective leadership fosters collaboration and creates alignment and harmony between the members of a group to work toward a common goal [34].

In a similar manner, Mayo and Woolley focus in their study on health care teams, on the important role of leadership in strengthening collective intelligence, given its role in the enhancement of inclusive collaboration and open communication, essential to allow each team member to share and apply their expertise to improve the achievement of the group’s goals [35]. Moreover, some studies evoke the role of transformational leadership in the development of collective intelligence [36]. Similarly, Maqbool et al. emphasize the significant impact of emotional intelligence, project manager skills, and transformational leadership on project success, suggesting that leaders who display transformational leadership behaviors can positively influence collective. These studies suggest that the adoption of transformational and adaptive leadership is fundamental to cultivating and improving collective intelligence in various organizations. Therefore, we do the fourth hypothesis below intelligence within their teams [37].

These studies show the adoption of changing and adaptive leadership is essential to cultivating and improving collective intelligence in various organizations.

2.5. Collaboration, Coordination, Cooperation

The concepts of collaboration, coordination, and cooperation, although often used interchangeably, have different meanings. Some studies emphasize this differentiation by placing collaboration at the top of a hierarchy that also includes communication, cooperation, and coordination [38]. This idea is supported by Gulati et al. [39], who see that collaboration is based on the interdependent pillars of cooperation and coordination.

Effective collaboration is based on multiple factors and conditions. Indeed, mutual commitment and alignment of objectives are the main elements [40]. Fuks et al. [41] see collaboration as an iterative process that includes communication, coordination, and cooperation.

In different contexts, the modalities of collaboration, coordination, and cooperation vary. Michaux et al. [42] observe how these interactions are part of various temporalities in a given territory. Alaloul et al. [43] apply these concepts to construction projects, highlighting the importance of the combined efforts of stakeholders. Wankmüller and Reiner [44] discuss the various ways in which the relationship between these concepts has been conceptualized in the literature, reflecting the complexity and variability of their application in different fields.

The progression from coordination to cooperation and ultimately to collaboration could be seen from an evolutionary and sociocultural angle. Griesemer and Shavit [45] argue that this sequence represents a crucial step in human sociocultural evolution, facilitating the transition to forms of collective individuality.

Therefore, several theoretical studies position collaborations as a catalyst for collective intelligence. Bedwell et al. [46] define collaboration as the process by which individuals mobilize their complementary expertise, highlighting results that they could not have achieved alone. This perspective highlights the role of collaboration in the synergy of individual contributions. Overall, this analysis of the literature reveals the complexity of the dynamics between collaboration and the emergence of forms of collective intelligence. To this end, our fifth hypothesis is reformulated in Table 1.

2.6. Knowledge Sharing

Knowledge sharing refers to the processes by which knowledge, especially tacit knowledge that is difficult to formalize, is communicated between individuals but also collected and exploited at the group and organizational level [47]. Numerous studies have demonstrated the decisive role of knowledge sharing in organizational performance, including by promoting innovation, creativity, and quality collective decision making [48,49]. With this in mind, knowledge sharing is essential to the emergence of collective intelligence, defined as the ability of a group of individuals to jointly solve complex problems [50].

Thus, several studies point out that organizational culture significantly influences the willingness of employees to share their knowledge. Kang et al. [51] highlight the role of organizational culture and reward systems in motivating knowledge sharing within a Taiwanese company. Similarly, Khan et al. [48] reveal that leadership strategies such as self-leadership are crucial to promoting knowledge sharing and ultimately innovative employee behavior.

On the individual psychological level, Obrenovic et al. [52] highlight that personality traits (especially consciousness) positively influence the sharing of tacit knowledge between SME employees, as well as the cooperative attitude and intrinsic motivation of individuals. Thus, Sung et al. [53] show that social comparison can arouse emotions of envy that reinforce the intentions of sharing knowledge.

In addition, many studies highlight the facilitating role of digital technologies, in particular, virtual communities, in the sharing of knowledge. In addition, Al Qahtani and Aksoy [54] highlight a whole range of collaborative tools that facilitate the transfer of knowledge in organizations.

In conclusion, this literature review highlighted the central role of knowledge sharing in allowing the emergence of collective intelligence, whether through technological, cultural, psychological, or managerial factors. Thus, we pose the sixth hypothesis in Table 1.

3. Methodology

3.1. Population and Sampling

The target population includes active Moroccan associations operating in various sectors such as education, health, sustainable development, and culture. These associations were selected in a reasoned way from databases, online directories, and searches on social networks. This approach made it possible to obtain a sample of 980 associations covering different regions of Morocco and various operational contexts (urban and rural). This method guarantees representativeness in terms of geographical and sectoral diversity, although not probabilistic.

3.2. Data Collection Tool

A structured questionnaire has been developed to measure the key dimensions of collective intelligence, based on scales recognized in the scientific literature. The questionnaire was designed in English and then translated into Arabic (both) to ensure better understanding by respondents. The translation was carried out by a bilingual expert, followed by validation by a panel of experts, including academics, specialists, and practitioners from the associative field, to ensure the cultural and conceptual relevance of the questions.

3.3. Data Collection

The data were collected between January and August 2024. The questionnaire was distributed by email, using the email addresses of the associations contained in the database. In order to optimize the participation rate, regular reminders were made by email and via WhatsApp. These channels have been chosen for their accessibility and popularity among members of the associations. A total of 125 valid responses were obtained, corresponding to a response rate of 12.75%. This rate is in line with expectations for similar research involving online surveys of associations.

3.4. Data Analysis

The collected data have been cleaned and prepared for statistical analysis. An exploratory factor analysis was conducted using SPSS (version 24) to identify the latent structures of the dimensions studied and to validate the measurement scales. Cronbach’s alpha coefficients were calculated to evaluate the internal reliability of the dimensions. Then the measurement model was assessed using SmartPLS 4 software to evaluate the convergent and discriminant validity of the dimensions. The relationships between the selected dimensions and the collective intelligence construct were then analyzed in order to evaluate their statistical significance and to confirm their contribution to the measurement of the construct.

4. Results

4.1. Descriptive Analysis

The sample of associations analyzed shows a great diversity in terms of size, seniority, and fields of action, reflecting a diversity of profiles in terms of size, seniority, and areas of intervention. Created between 1929 and 2022, these structures reflect the coexistence of older associations and, more recently, created organizations with a mix of well-established organizations and recent initiatives to address emerging challenges. Older associations (created before 2000) coexist with more recent associations (created after 2010), illustrating the diversity of the organizational profiles of the sample. The size of the associations remains modest, with an average of 11 active members, but they mobilize a large network of 190 members on average, which testifies to the importance of their membership base in relation to the average number of active members.

Located in rural and semi-rural fields areas, such as Taroudante, Chtouka Ait Baha, Tata, etc., associations meet fundamental needs, including having water DWS, local development, and social cohesion. These sectors reflect the priority axes of the regions concerned, which frequently face inaccessibility to social services and limited access to infrastructure. Local development initiatives include projects to improve infrastructure (roads and access to drinking water), support agricultural activities, and provide training. The presence of projects related to the community management of drinking water reflects the importance given to this theme in several territories covered by the sample. Furthermore, the commitment of the members is a major asset: respondents say they are well informed about the rules of conduct and strongly involved in projects. These descriptive characteristics highlight the organizational diversity of the associations studied as well as their level of involvement in projects carried out within their communities.

4.2. Exploratory Factor Analysis

4.2.1. Factor Analysis of First-Order Constructs

• Social Sensitivity:

According to the iterative method, the items SenSocial.8, SenSocial.7, SenSocial.6, and SenSocial.4 were deleted during the main component analysis. These items had weaknesses in factor loads or high levels of uniqueness, which compromised the one-dimensionality and validity of the social sensitivity dimension.

The internal consistency of the remaining indicators mentioned in Table 2 (Sensi.2, Sensi.3, Sensi.5, and Sensi.1) is evaluated using the Cronbach’s alpha test, which has a coefficient of 0.735. This result reflects an acceptable internal consistency, proposing that the items selected have an acceptable internal consistency to measure the social sensitivity dimension. Similarly, the KMO index, with a value of 0.715, confirms that the data are adequate to perform a factor analysis. This shows that the correlations between the manifest variables make it possible to extract a significant latent factor. Moreover, the percentage of variance clarified by the main factor is 57.254%. This means that a significant proportion of the total variance is explained by a single factor, which supports the one-dimensionality of the dimension being studied. Overall, the results support the one-dimensionality of the dimension. However, the low Sensi.1 indicator (0.268) indicates that this item is insufficiently represented by the extracted factor and requires re-evaluation during confirmatory analysis.

Table 2. Analysis of the items of the social sensitivity dimension.

Items	Extraction	Alpha of Cronbach	KMO	% Variance
Sensi.2	0.755	0.735	0.715	57.254
Sensi.3	0.648
Sensi.5	0.620
Sensi.1	0.268

2.

Autonomy:

According to the iterative method, the Auton.1 item was deleted during the main component analysis. This item had weaknesses in factor loads or high levels of uniqueness, which compromised the unidimensionality and validity of the construct.

The internal consistency of the remaining indicators stated in Table 3 (Auton.2, Auton.3, Auton.4, and Auton.5) is analyzed using the Cronbach’s alpha test, which has a coefficient of 0.775. This result reflects well internal consistency, proposing that the selected items have an acceptable internal consistency to measure the autonomy dimension. Similarly, the KMO index, with a value of 0.756, proves that the data are adequate to perform a factor analysis. This shows that the correlations between the manifest variables make it possible to extract a significant latent factor. Besides, the percentage of variance clarified by the main factor is 60.337%. This means that a substantial proportion of the total variance in responses is attributable to a single latent factor. This result supports the one-dimensional structure of the measurement.

Table 3. Analysis of the items of the autonomy dimension.

Items	Extraction	Alpha of Cronbach	KMO	% Variance
Auton.4	0.698	0.775	0.756	60.337
Auton.3	0.709
Auton.5	0.482
Auton.2	0.524

3.

Motivation:

According to the iterative method, the Motiv.2 and Motiv.3 items were removed during the main component analysis. These items had weaknesses in factor loads or high levels of uniqueness, which compromised the unidimensionality and validity of the build.

The internal consistency of the residual indicators mentioned in Table 4 (Motiv.4, Motiv.6, Motiv.5, and Motiv.1) is evaluated using the Cronbach’s alpha test, which has a coefficient of 0.805. This result shows a very good internal consistency, suggesting that the selected items have an acceptable internal consistency to measure the motivation dimension. Similarly, the KMO index, with a value of 0.783, proves that the data are adequate to perform a factor analysis. Moreover, the percentage of variance clarified by the main factor is 63.307%. This means that a significant proportion of the total variance of responses is caused by a single latent factor. This percentage testifies to the validity of the measure and the relevance of the items selected.

Table 4. Analysis of the items of the motivation dimension.

Items	Extraction	Alpha of Cronbach	KMO	% Variance
Motiv.4	0.658	0.805	0.783	63.307
Motiv.6	0.597
Motiv.5	0.682
Motiv.1	0.596

4.

Leadership:

According to the iterative method, the items Leadership.4 and Leadership.5 were removed during the main element analysis. These items had weaknesses in factor loads or high levels of uniqueness, which compromised the unidimensionality and validity of the construct.

The internal consistency of the residual indicators mentioned in Table 5 (Leadership.2, Leadership.3, Leadership.1, and Leadership.6) is assessed using the Cronbach’s alpha test, which has a coefficient of 0.898. This result reflects excellent internal consistency, suggesting that the selected items have an acceptable internal consistency to measure the leadership dimension. Similarly, the KMO index, with a value of 0.831, proves that the data collected is adequate to show a factor analysis. However, the percentage of variance proved by the main factor is 76.876%. This means that a very large proportion of the total variance of responses is attributable to a single latent factor.

Table 5. Analysis of the items of the transformational leadership dimension.

Items	Extraction	Alpha of Cronbach	KMO	% Variance
Leadership.2	0.850	0.898	0.831	76.876
Leadership.3	0.744
Leadership.1	0.745
Leadership.6	0.737

5.

Collaboration

According to the iterative method, several items were removed during the main element analysis. Items Collab.1, Collab.2, Collab.3, Collab.4, Collab.5, Collab.8, Collab.9, Collab.10, Collab.11, Collab.15, and Collab.17 have been excluded. These items had low factor loads or high levels of uniqueness, therefore compromising the unidimensionality and validity of the construct.

The internal consistency of the indicators selected (Collab.6, Collab.7, Collab.12, Collab.13, Collab.14, Collab.16, and Collab.18) was analyzed using the Cronbach’s alpha test, which has a coefficient of 0.921. This result testifies to excellent internal consistency, confirming that these items have an acceptable internal consistency for measuring the collaboration dimension.

Similarly, the KMO index displayed in Table 6, with a value of 0.930, shows that the data are particularly adequate for carrying out a factor analysis. This means that the connections between the manifest variables make it possible to extract a significant latent factor. Moreover, the percentage of variance explained by the main factor is 68.538%. This suggests that a significant proportion of the total variance of responses is attributable to a single latent factor. This percentage reinforces the validity of the measure and underlines the relevance of the items selected.

Table 6. Analysis of the items of the collaboration dimension.

Items	Extraction	Alpha of Cronbach	KMO	% Variance
Collab.12	0.612	0.921	0.930	68.538
Collab.18	0.688
Collab.13	0.707
Collab.16	0.741
Collab.14	0.724
Collab.7	0.681
Collab.6	0.644

6.

Knowledge Sharing:

According to the iterative method, several items were removed during the main component analysis. Items KS.3, KS.4, KS.5, KS.6, KS.7, KS.8, KS.9, KS.10, and KS.11 have been removed. These items had low factor loads or high levels of uniqueness, therefore compromising the unidimensionality and validity of the construct.

The internal consistency of the indicators selected (KS.12, KS.13, KS.2, and KS.1) was evaluated using the Cronbach’s alpha test, which presents a coefficient of 0.820. This result testifies to better internal consistency, confirming that these items reliably measure the concept of knowledge sharing.

Similarly, the KMO index displayed in Table 7, with a value of 0.74, confirms that the data are adequate to show a factor analysis. This means that the correlations between the observed variables are sufficiently high to justify the performance of the factor analysis. Also, the percentage of variance clarified by the main factor is 65.494%. This signals that a significant proportion of the total variance of responses is attributable to a single latent factor. This percentage reinforces the validity of the measure and underlines the relevance of the items selected.

Table 7. Analysis of the items of the knowledge sharing dimension.

Items	Extraction	Alpha of Cronbach	KMO	% Variance
KS.12	0.720	0.820	0.74	65.494
KS.13	0.639
KS.2	0.680
KS.1	0.581

4.2.2. Exploratory Factor Analysis of the Multidimensional Structure of Collective Intelligence

Collective intelligence is conceptualized as a multidimensional construct composed of six first-rate dimensions: social sensitivity, autonomy, motivation, transformational leadership, collaboration, and knowledge sharing.

A six-dimensional factorial solution was examined to assess the correspondence between the empirical structure of the data and the six theoretically selected dimensions. The matrix of factor loads after rotation was then analyzed to verify that each item had its main load on the expected dimension and limited secondary loads on the other dimensions. Items with low loads or high cross-loads were reassessed to maintain clarity of the factor structure.

Finally, an overall Cronbach’s alpha (Table 8) was calculated in order to assess the internal consistency of all the selected items. This indicator provides an overall estimate of the internal consistency of the instrument, without constituting in itself a proof of the validity of the multidimensional construct of collective intelligence.

Table 8. Reliability and adequacy statistics for all items.

Number of Items	Alpha of Cronbach	KMO	% Variance
27	0.894	0.852	61.320

The results of the exploratory factor analysis support the internal coherence of the proposed multidimensional structure for collective intelligence. The overall Cronbach’s alpha (0.894) indicates a good internal consistency of all the selected items, while the KMO index (0.852) confirms the adequacy of the data to the factor analysis. Moreover, the factor solution explains 61.320% of the total variance, which is a satisfactory result for an exploratory analysis and supports the relevance of the factor structure chosen.

These results are corroborated by the analysis of the matrix of factor loads after rotation in Table 9. The selected items as a whole have higher loads on the dimension to which they are theoretically attached than on the other dimensions, which supports the proposed multidimensional structure. The few observed cross-loads remain limited and will be further evaluated during the confirmatory analysis.

Table 9. Matrix of components after rotational.

	1	2	3	4	5	6
Collab.12	0.890	0.154	0.217	0.198	0.022	0.023
Collab.18	0.851	0.128	0.113	0.023	0.091	0.042
Collab.13	0.763	0.097	0.021	0.082	0.042	0.008
Collab.16	0.735	0.261	0.042	0.074	0.079	0.035
Collab.14	0.706	0.192	0.270	0.075	0.108	0.086
Collab.7	0.650	0.132	0.046	0.065	0.038	0.110
Collab.6	0.631	0.077	0.155	0.196	0.074	0.270
Leadership.2	0.005	0.947	0.029	0.023	0.071	0.026
Leadership.3	0.024	0.757	0.146	0.067	0.141	0.012
Leadership.1	0.085	0.698	0.053	0.017	0.032	0.130
Leadership.6	0.332	0.634	0.037	0.154	0.046	0.170
KS.12	0.024	0.079	0.807	0.067	0.038	0.205
KS.13	0.138	0.202	0.665	0.231	0.018	0.008
KS.2	0.128	0.127	0.664	0.005	0.036	0.054
KS.1	0.189	0.238	0.566	0.019	0.004	0.147
Motiv.4	0.018	0.008	0.006	0.804	0.124	0.118
Motiv.6	0.213	0.129	0.007	0.713	0.070	0.032
Motiv.5	0.183	0.213	0.146	0.631	0.121	0.017
Motiv.1	0.208	0.069	0.214	0.539	0.054	0.188
Auton..4	0.070	0.051	0.010	0.081	0.817	0.020
Auton..3	0.021	0.050	0.001	0.085	0.772	0.061
Auton..5	0.074	0.226	0.037	0.086	0.583	0.038
Auton.2	0.196	0.154	0.039	0.074	0.567	0.023
Sensi.2	0.002	0.034	0.010	0.132	0.028	0.849
Sensi.3	0.017	0.016	0.254	0.067	0.056	0.601
Sensi.5	0.273	0.177	0.293	0.048	0.005	0.554
Sensi.1	0.011	0.051	0.117	0.133	0.017	0.388

4.2.3. Synthesis of the Results of Exploratory Factor Analysis

During our exploratory approach, our main objective was to validate the measurement instrument that we had developed from the scales developed following an in-depth literature review. This validation depended on a rigorous methodological process, including several important steps. First, an in-depth evaluation of the measured constructs was carried out in order to examine the internal consistency and robustness of the measurement scales. An iterative analysis was conducted to identify and eliminate problematic items, whether they are low-representative or insufficient reliability.

At the end of this methodical process summarized in Table 10, the constructs demonstrated solid internal consistency and overall consistency, thus supporting their relevance for later use in a measurement model. However, in order to further strengthen the validity and robustness of these measures, additional validation by a confirmatory factor method is envisaged. We have therefore planned to use the SmartPLS software to perform a confirmatory factor analysis. This step will make it possible to evaluate and confirm the relevance of the measurement model in a more specific theoretical and practical framework, thus consolidating the reliability and validity of our instruments.

Table 10. Matrix of components after rotational.

Built in Second Order	Alpha Second Order	KMO Second Order	% Second Order	First-Order Contruits	Nbr of Item Selected	Nbr of Deleted Items	Alpha of Cronbach	KMO	% Variance
Collective intelligence	0.894	0.852	61.320	Social Sensitivity	4	4	0.735	0.715	57,254
				Autonomy	4	1	0.775	0.756	60,337
				Motivation	4	5	0.805	0.783	63,307
				Leadership	4	2	0.898	0.831	76,876
				Collaboration	7	11	0.921	0.930	68,538
				Knowledge sharing	4	14	0.820	0.740	65,494

4.3. Confirmatory Factor Analysis

4.3.1. Validation of First-Order Constructs

• Composite Reliability and Convergent Validity:

The internal reliability of the constructs is generally satisfactory, with Cronbach’s alpha and composite reliability values better than 0.70 for all constructs, indicating good internal consistency of the items. Moreover, according to Table 11, the convergent validity, measured by the average extracted variance (AVE), is not enough for the constructs autonomy (0.483) and social sensitivity (0.460) because their ELAs are below the recommended threshold of 0.50, suggesting that the items associated with these constructs share an insufficient variance. Conversely, the collaboration, KS, leadership, and motivation constructs have an adequate convergent validity (AVE ≥ 0.50), which confirms that their items measure the concepts they are supposed to represent. These results call for a reassessment of the items built with low AVE to guarantee the theoretical and empirical robustness of the model.

Table 11. Reliability and convergent validity of the measurement model.

	Cronbach’s Alpha	Composite Reliability (CR)	Average Variance Extracted (AVE)
Auton.	0.778	0.785	0.483
Collab.	0.923	0.921	0.631
KS	0.824	0.825	0.543
Leadership.	0.899	0.900	0.697
Motiv.	0.806	0.809	0.509
Sensi.	0.739	0.752	0.460

After the removal of the Sensi.1 and Auton.5 items, the metrics present a clear improvement in the convergent validity of the constructs concerned. According to Table 12, the AVE for autonomy has increased from 0.483 to 0.541 and, for social sensitivity, from 0.460 to 0.571, now reaching the recommended threshold of 0.50. These results indicate that the remaining items capture a sufficient proportion of the common variance of their respective constructs. Besides, internal reliability, measured by Cronbach’s alpha and composite reliability remain high for all constructs, with values better than 0.70. The other constructs, for instance, collaboration, KS, leadership, and motivation, retain their robustness, with AVE well beyond the minimum required threshold. Thus, these adjustments reinforce the reliability and overall validity of the model, making it ready for final validation.

Table 12. Reliability and convergent validity of the measurement model after deletion of the two items.

	Cronbach’s Alpha (Standardized)	Cronbach’s Alpha (Unstandardized)	Composite Reliability (rho_c)	Average Variance Extracted (AVE)
Auton.	0.772	0.763	0.770	0.541
Collab.	0.923	0.921	0.921	0.631
KS	0.824	0.820	0.825	0.543
Leadership.	0.899	0.898	0.900	0.697
Motiv.	0.806	0.805	0.809	0.509
Sensi.	0.792	0.790	0.799	0.571

2.

Discriminatory Validity

The results of the Fornell–Larcker criterion presented in Table 13 show that all the constructs of the model respect the discriminating validity. The square root of the AVE for each construct (diagonal values) is better than the correlations between this construct and the others (non-diagonal values). For example, for autonomy, the square root of the AVE is 0.735, which is higher than its correlations with other constructs, such as collaboration (0.301) and KS (0.128). Similarly, the constructs collaboration, leadership, motivation, KS, and social sensitivity also meet this criterion, with square roots of AVE ranging from 0.714 to 0.835, all higher than the respective correlations. These results confirm that each construct is distinct from the others and that the items associated with a construct measure it more than any other. This reinforces the conceptual and empirical robustness of the model by validating its latent structure.

Table 13. Fornell–Larcker criterion of discriminatory validity.

	Auton.	Collab.	KS	Leadership.	Motiv.	Sensi.
Auton.	0.735
Collab.	0.301	0.795
KS	0.128	0.396	0.737
Leadership.	0.194	0.735	0.433	0.835
Motiv.	0.125	0.110	0.605	0.340	0.714
Sensi.	0.136	0.603	0.438	0.612	0.382	0.756

The cross-loading criterion is used to evaluate discriminating validity by ensuring that each item loads more heavily on its own built than on others. The results mentioned in Table 14 show that most items meet this criterion, such as Auton.3 (0.878 on autonomy), Collab.16 (0.865 on collaboration), and Leadership.3 (0.862 on leadership), confirming that these items mainly measure their own dimension. The loads on the other constructs are low, thus supporting the distinction between dimensions. Regarding the motivation dimension, all items, including Motiv.4 (0.750), load satisfactorily on their own built, demonstrating their discriminatory validity. Also, some items, such as Collab.7, show relatively high loads on another built (e.g., 0.825 on collaboration and 0.581 on leadership), which might suggest a significant correlation between these dimensions. Overall, the results indicate that discriminating validity is widely respected, although some minor adjustments may be considered to strengthen the robustness of the model.

Table 14. Cross-loadings for the assessment of the discriminating validity of constructs.

	Auton.	Collab.	KS	Leadership.	Motiv.	Sensi.
Auton.3	0.878	0.259	0.105	0.221	0.158	0.117
Auton.4	0.739	0.133	0.042	0.040	0.025	0.026
Auton.2	0.841	0.280	0.131	0.161	0.092	0.169
Collab.12	0.294	0.775	0.144	0.407	0.120	0.401
Collab.13	0.213	0.839	0.279	0.571	0.030	0.406
Collab.14	0.178	0.856	0.474	0.652	0.149	0.423
Collab.16	0.194	0.865	0.336	0.698	0.154	0.503
Collab.18	0.344	0.828	0.328	0.467	0.079	0.403
Collab.6	0.260	0.803	0.361	0.464	0.029	0.546
Collab.7	0.230	0.825	0.224	0.581	0.075	0.446
KS.1	0.115	0.446	0.806	0.470	0.290	0.254
KS.12	0.100	0.269	0.839	0.317	0.455	0.376
KS.13	0.135	0.249	0.771	0.215	0.459	0.220
KS.2	0.046	0.205	0.813	0.310	0.353	0.254
Leadership.1	0.082	0.561	0.295	0.860	0.209	0.517
Leadership.2	0.080	0.588	0.386	0.921	0.277	0.512
Leadership.3	0.247	0.547	0.417	0.862	0.255	0.448
Leadership.6	0.270	0.650	0.371	0.863	0.278	0.397
Motiv.1	0.026	0.025	0.426	0.234	0.761	0.286
Motiv.4	0.011	0.014	0.308	0.123	0.750	0.148
Motiv.5	0.141	0.092	0.457	0.312	0.848	0.248
Motiv.6	0.156	0.197	0.294	0.208	0.807	0.224
Sensi.2	0.124	0.444	0.357	0.454	0.375	0.885
Sensi.3	0.054	0.367	0.417	0.379	0.224	0.797
Sensi.5	0.150	0.543	0.108	0.506	0.136	0.838

A third criterion of discriminating validity is that of HTMT (heterotrait-monotrait ratio), which requires that the ratios between constructs be less than 0.85 to confirm their distinction. The results of this criterion displayed in Table 15 show that this criterion is met for all built, with moderate HTMT ratios, such as between autonomy and collaboration (0.326) or leadership and collaboration (0.727), and low, as between autonomy and social sensitivity (0.182). No HTMT value exceeds the critical threshold, confirming that the constructs are conceptually distinct and that the model fully meets the requirements of discriminating validity, reinforcing its reliability and theoretical robustness.

Table 15. Discriminating validity—heterotrait-monotrait ratio (HTMT).

	Auton.	Collab.	Leadership.	Motiv.	Sensi.	KS
Auton.
Collab.	0.326
Leadership.	0.210	0.727
Motiv.	0.159	0.149	0.323
Sensi.	0.182	0.628	0.631	0.355
KS	0.150	0.407	0.470	0.582	0.430

4.3.2. Validation Measure Model:

• Significance Test:

The validation of developed formative “collective intelligence” is based on the significance test of the explanatory dimensions identified in the model (Table 16): autonomy, collaboration, knowledge sharing (KS), leadership, motivation, and social sensitivity. The hypotheses associated with this test are formulated as follows:

Table 16. Significance test of the effect of the CI dimensions.

Hypotheses	Dimensions	Poids (O)	Bootstrap Mean (M)	T Statistics	p-Values	Decision
H2	Auton	0.089	0.091	2.781	0.005	Supported
H5	Collab	0.487	0.481	13.742	0.000	Supported
H6	KS	0.179	0.177	6.383	0.000	Supported
H4	Leadership	0.300	0.298	10.458	0.000	Supported
H3	Motiv	0.107	0.104	3.166	0.002	Supported
H1	Sensi	0.180	0.177	8.250	0.000	Supported

H0. 

No significant effect of the variable (γ = 0).

H1. 

The effect of the variable is significant (γ ≠ 0).

For this validation, a bootstrapping technique was carried out with 2000 subsamples, thus guaranteeing sufficient precision in the assessment of the parameters. The confidence level was set at 95%, using a bootstrap-based trust interval (Percentile Bootstrap), end-of-limit potential biases in estimates.

Standardized regression coefficients (γ), Student statistics (T statistics), and significance thresholds (p-value) were calculated using smartPls. The analysis of the results reveals that all dimensions have a significant effect (p-value < 0.05) on the variable “collective intelligence”.

By analyzing the signs of the standardized coefficients, we can see that the significant dimensions all have positive effects on the variable “collective intelligence.” These vary between 0.091 for the “autonomy” dimension and 0.487 for the “collaboration” dimension, which represents the strongest effect. The leadership dimension follows with a coefficient of 0.300, while the knowledge sharing (0.179) and social sensitivity (0.177) dimensions make moderate contributions. Finally, the “motivation” dimension has a smaller but significant positive effect, with a coefficient of 0.107. These results confirm that all dimensions contribute positively to the explanation of “collective intelligence,” with a particular emphasis on collaboration and leadership as main drivers.

• Multicollinearity Test

The collinearity test through the variance inflation factor (VIF) values makes it possible to assess whether independent variables in the model have excessive redundancy, which might compromise the stability and interpretation of the regression coefficients. According to Table 17, VIF values less than 3 indicate negligible collinearity, ensuring that each variable contributes separately to the model. In the results, all dimensions (autonomy, collaboration, leadership, motivation, social sensitivity, and knowledge sharing) have VIF values between 1123 (autonomy) and 2229 (collaboration), which is good below the critical threshold. This confirms that the collinearity in the model is low and does not negatively affect structural relationships. These results reinforce the validity and reliability of estimates in the “collective intelligence” model.

Table 17. VIF (variance inflation factor) of the explanatory variables of “collective intelligence”.

Autonomie	1.106
Collaboration	2.246
Knowledge sharing	1.521
Leadership	2.106
Motivation	1.420
Social sensitivity	1.608

5. Discussion

The results of this study provide empirical support for a multidimensional conception of collective intelligence in the context of Moroccan associations. The analyses carried out lead to six constituent dimensions: social sensitivity, autonomy, intrinsic motivation, transformational leadership, collaboration, and knowledge sharing. All have positive and statistically significant weights in the formation of the studied construct, although their relative contribution differs. However, these results must be interpreted within the precise framework of the chosen operationalization: collective intelligence is understood here from respondents’ perceptions of the collective functioning of their association and not from an objective measure of collective performance.

This precision makes it possible in particular to situate the results in relation to the work of Woolley et al. [6]. In this work, collective intelligence is understood as a general factor of performance emerging from the results obtained by the groups at different tasks. This research adopts a different and complementary perspective, based on the perceptual evaluation of organizational and relational dimensions likely to characterize collective functioning. The results, therefore, do not allow direct assimilation of the measured construct to the collective performance factor identified by [6], but propose a multidimensional operationalization adapted to the studied associative context.

Social sensitivity is the first significant dimension of the construct, with a weight of 0.180. This result is consistent with the work of [6], which highlighted an association between the average social sensitivity of members and collective performance. However, in the context of this study, this finding does not mean that social sensitivity “causes” collective intelligence. Rather, it indicates that the ability of members to perceive and interpret social and emotional signals is a component of how collective intelligence is understood in the associations studied. This dimension thus emphasizes the importance of the relational component in collective functioning.

Autonomy has a lower weight (0.089), but is statistically significant. Its integration into the construct can be compared to the principle of independence put forward by Surowiecki [7], according to which the quality of a collective process depends in particular on the possibility for individuals to retain a certain independence of judgment. In the context of associations, autonomy can thus be understood as the possibility given to members to express their perspectives, to take initiatives, and to contribute individually to collective action. The relative weakness of its weight, nevertheless, invites us to avoid assigning it a decisive role and to consider its contribution in conjunction with the other dimensions of the construction.

Intrinsic motivation is also a significant dimension, with a weight of 0.107. This result can be compared to the theory of self-determination [18] and the work of Amabile [19], which emphasize the role of intrinsic motivation in commitment and creativity. This dimension is of particular importance in associations, whose functioning is largely based on the commitment of the members. The results obtained, therefore, support the integration of intrinsic motivation into the proposed structure, without making it possible to affirm that an increase in motivation would mechanically lead to an increase in collective intelligence.

Transformational leadership has the second-highest relative weight (0.300). This result is consistent with work that combines transformational leadership with member engagement, building a shared vision and team processes, including Dionne et al. [55]. In this research, however, leadership must be interpreted as a constituent dimension of the collective functioning studied and not as a causal antecedent of collective intelligence. His relatively important contribution suggests that the ability to mobilize members around common goals occupies a notable place in the observed empirical structure.

Collaboration has the highest relative weight (0.487). This result is consistent with the place given to collaborative processes in the literature on collective functioning and with the work of Bedwell et al. [46]. The coordination of actions, interactions between members, and the pooling of skills thus appear to be strongly represented in the operationalization of the proposed collective intelligence.

However, this outcome should be interpreted with particular caution, given the empirical proximity between collaboration and transformational leadership. The correlation observed between these two dimensions (0.735), as well as certain cross-loads, notably that of Collab.7, suggests a not insignificant overlap. Although the Fornell–Larcker and HTMT criteria generally support their discriminatory validity, the results do not allow a conceptual and empirical proximity between these two dimensions to be totally ruled out. This is particularly important as collaboration and leadership carry the greatest weight. Subsequent research on independent samples will therefore have to verify whether this distinction remains stable in other associative contexts.

Our results, which place collaboration (0.487) and transformational leadership (0.300) at the forefront of the relative weights of the constituent dimensions, can be put into perspective with the work of [56]. Using data from 22 studies involving 5279 individuals in 1356 groups, these authors show in particular the importance of collaborative processes in the collective intelligence of groups. However, this approach must be interpreted with caution since [56] understand collective intelligence from the performance of groups at different tasks, while our research is based on perceptual and multidimensional operationalization. More recently, Aubouin-Bonnaventure et al. [57] proposed a multilevel conceptualization of collective intelligence and developed a scale for work teams, thus providing a recent point of comparison with our measurement approach. However, differences in conceptualization, operationalization, and organizational context do not allow a direct equivalence between these results to be established. Given also the size of our sample (n = 125) and its non-probabilistic nature, these reconciliations should be considered as perspectives rather than external confirmations of our model.

Knowledge sharing is also a significant dimension, weighing 0.179. This result joins the work of Mačiulienė and Skaržauskienė [50], which highlight the importance of knowledge circulation and aggregation in collective intelligence processes. In associations, this dimension refers in particular to exchanges of experience, the transmission of know-how, and the sharing of information necessary for collective action. It thus supplements the relational dimensions with a more cognitive and informational component. In short, collective intelligence can be understood as a dynamic based on the articulation of several mechanisms, such as autonomy, motivation and social sensitivity, and organizational factors. It thus refers to a collective dynamic in which collaboration, knowledge sharing, and synergy between members play an important role [58].

The results can also be interpreted from a perspective articulating different levels of analysis. At the micro level, social sensitivity, autonomy, and motivation refer mainly to the characteristics and behaviors of the members. At the meso level, collaboration, knowledge sharing, and transformational leadership are more characteristic of the association’s internal operating processes. The macro level, particularly in relation to relations with public institutions, inter-association networks, and the institutional environment, is not directly measured in this research. This limit opens up an important perspective for future studies aimed at repositioning collective associative intelligence in its institutional and territorial environment.

These results must also be assessed in the light of the scaling process. In particular, a significant number of items were removed for collaboration and knowledge sharing. While this procedure yielded satisfactory psychometric properties in the sample studied, it may also have altered the conceptual domain covered by the original instruments. The dimensions obtained must therefore be considered as an empirical adaptation to the context studied rather than as an integral reproduction of the original scales. The replication of this structure on a new sample is therefore a necessary step to examine its stability.

Another important limitation is the size of the sample. The results are based on 125 responses obtained from non-probabilistic sampling and a response rate of 12.76%. They cannot, therefore, be generalized to all Moroccan associations. A non-response bias cannot be totally excluded since the associations that replied to the questionnaire may differ from those that did not participate. The results should therefore be seen as a first empirical support for the proposed structure rather than as a definitive validation applicable to the entire Moroccan voluntary sector.

Finally, this study opens several research perspectives. The proposed structure should be re-evaluated on larger, independent samples and, where possible, using a probabilistic sampling procedure. Mixed approaches combining questionnaires, interviews, and observations would also deepen the understanding of how these dimensions manifest themselves in associative practices. Above all, future research could explicitly distinguish the organizational and relational dimensions measured here from an external collective outcome, integrating indicators such as quality of decision making, collective problem solving, innovation, or organizational performance. Such an approach would then make it possible to examine whether and to what extent the dimensions identified are associated with objective manifestations of collective performance.

In short, the results support a multidimensional structure of collective intelligence articulating individual and relational dimensions of social sensitivity, autonomy and motivation, and more organizational dimensions of transformational leadership, collaboration, and knowledge sharing. Collaboration and leadership have the highest relative weights, but they should not be interpreted as the “determinants” or root causes of collective intelligence. The results indicate rather their relative importance in the formation of the construct as it is operationalized in this research. This interpretation is consistent with the objective now focused on the development and validation of a multidimensional measurement scale of collective intelligence in the context of Moroccan associations.

6. Conclusions

This research contributes to the understanding and measurement of collective intelligence within Moroccan associations by proposing a multidimensional structure adapted to this context. From the theoretical foundations mobilized and the factorial analyses carried out, six constituent dimensions were selected: social sensitivity, autonomy, intrinsic motivation, transformational leadership, collaboration, and knowledge sharing. The results obtained thus provide empirical support for the conceptualization of collective intelligence as a multidimensional construct in the Moroccan associative context.

Among the constituent dimensions selected, collaboration and transformational leadership have the highest relative weights in the formation of the multidimensional construct. Collaboration includes the importance of interaction, coordination, and pooling of member skills, while transformational leadership refers to the ability to engage members around a shared vision and goals. However, these results should not be interpreted as causal effects of these dimensions on collective intelligence, but as their relative contribution to the formation of the studied construct.

Social sensitivity and knowledge sharing are also important dimensions of the chosen structure. Social sensitivity refers in particular to the ability of members to perceive and interpret social and emotional signals and to facilitate their interactions, while knowledge sharing reflects the circulation, exchange, and sharing of knowledge within the association. Their integration into the chosen structure thus underlines the relational and cognitive character of the collective intelligence studied.

Autonomy and intrinsic motivation complete this multidimensional structure. Autonomy reflects the ability of members to contribute independently while participating in collective action, while intrinsic motivation refers to their commitment and involvement in the activities of the association. The results support the integration of these two dimensions in the measurement of collective intelligence, without, however, allowing to conclude a causal effect of them on the latter.

Theoretically, this research contributes to the literature by proposing a multidimensional operationalization of collective intelligence adapted to the particular context of Moroccan associations. In particular, it makes it possible to distinguish the approach adopted in this study from the conceptions of collective intelligence based on performance in collective tasks. Collective intelligence is understood here through members’ perceptions of different dimensions of collective functioning. This distinction invites us to interpret the results as a perceptual and multidimensional measure of collective intelligence and not as a direct measure of collective performance.

On a practical level, the proposed instrument offers a grid allowing Moroccan associations to understand different dimensions associated with their collective functioning. It can be used to diagnose practices related to collaboration, transformational leadership, knowledge sharing, autonomy, motivation, and social sensitivity. The results can thus guide the reflection of associative leaders on organizational practices likely to support collective dynamics, without, however, making it possible to establish directly their effect on organizational performance.

However, this study has several limitations that need to be taken into account when interpreting the results. First, the sample includes 125 valid responses and is based on a non-probabilistic sampling procedure, which limits the generalization of the results to all Moroccan associations. Second, the response rate of 12.76% exposes the study to a potential non-response bias, as participating associations may have different characteristics than non-responding associations. Thirdly, factorial cleansing has led to the deletion of several items, particularly for the collaborative and knowledge sharing dimensions. Although this procedure has improved the psychometric properties of the scales in the sample studied, it may have partially modified the content of the original instruments and therefore requires replication on an independent sample.

Finally, future research should confirm the proposed multidimensional structure with larger, probabilistic, and diversified samples. They could also mobilize mixed approaches combining questionnaires, interviews, and observations in order to deepen the understanding of collective mechanisms within associations. The integration of objective indicators, such as organizational performance, quality of decision making, collective problem-solving capacity or innovation, would also make it possible to examine the relationships between the dimensions identified and distinct manifestations of collective performance. Such research would allow us to go beyond the limits of an exclusively perceptual measure and to better approximate the approach developed here to work based on collective performance.

In short, this study provides the first empirical support for a multidimensional measure of collective intelligence in the context of Moroccan associations. The six dimensions identified—social sensitivity, autonomy, intrinsic motivation, transformational leadership, collaboration, and knowledge sharing—constitute the proposed structure of the instrument. The results must nevertheless be interpreted within the limits of the sample and the methodological framework chosen. This research is thus a step in the development and validation of an instrument for measuring collective intelligence adapted to the Moroccan associative context, whose stability and generalization will have to be examined in future research.